Evolutionary-Rough Feature Selection for Face Recognition

Abstract

Elastic Bunch Graph Matching is a feature-based face recognition algorithm which has been used to determine facial attributes from an image. However the dimension of the feature vectors, in case of EBGM, is quite high. Feature selection is a useful preprocessing step for reducing dimensionality, removing irrelevant data, improving learning accuracy and enhancing output comprehensibility. In rough set theory reducts are the minimal subsets of attributes that are necessary and sufficient to represent a correct decision about classification. The high complexity of the problem has motivated investigators to apply various approximation techniques like the multi-objective GAs to find near optimal solutions for reducts. We present here an application of the evolutionary-rough feature selection algorithm to the face recognition problem. The input corresponds to biometric features, modeled as Gabor jets at each node of the EBGM. Reducts correspond to feature subsets of reduced cardinality, for efficiently discriminating between the faces. The whole process is optimized using MOGA. The simulation is performed on large number of Caucasian and Indian faces, using the FERET and CDAC databases. The merit of clustering and their optimality is determined using cluster validity indices. Successful retrieval of faces is also performed.